Region-balanced Loss.
In image segmentation, Dice and Jaccard indices (IoU) are commonly used as the evaluation metrics. However, the most common training criterion, cross entropy, does not directly optimize these metrics. In medical imaging, researchers propose to optimize soft/surrogate version of these indices. SoftIOU [1]} proposes to optimize a soft version of the Jaccard index; Lovasz Softmax [2]} also optimizes the Jaccard index based on the Lovasz convex extension; SoftDice [3]} and softTversky [4]} optimize a soft version of the Dice index and Tversky index (see Table REF ). However, concerns have been raised in [5]} on whether these losses consider the trade-off between false positives and false negatives. We show that they also tend to yield high mean accuracy at the expense of lower mean IoU.
